Long term interest rates - GDP - debt-to-GDP ratio

Data cleaning

Uploading data

total <- read_csv("data/LTINT_GDP.csv")
df_gdp <- read_csv("data/GDP_yearly.csv")
df_ltint <- read_csv("data/LTINT_yearly.csv")

Making data wide I removed the Flag Codes to make the wide frames more manageable. Flags were not necessary for the analysis.

df_ltint_wide <- df_ltint %>% 
  select(-`Flag Codes`) %>% 
  pivot_wider(names_from = INDICATOR, values_from = Value)

Using MEASURE variable as the pivot to make it wide (names_from = MEASURE) because there were two different measures of GDP (million USD and per capita) Then dropping the INDICATOR variable that had GDP as values

df_gdp_wide <- df_gdp %>% 
  select(-`Flag Codes`) %>% 
  pivot_wider(names_from = MEASURE, values_from = Value, names_prefix = "GDP_") %>% 
  select(-INDICATOR)

Merging GDP and long term interest rates in one dataframe

df_merged <- df_gdp_wide %>% left_join(df_ltint_wide %>% select(LOCATION, TIME, LTINT), by = c("LOCATION","TIME"))

df_debt_ratio <- read_csv("data/debt_gdp_ratio_oecd.csv")

## Parsed with column specification:
## cols(
##   LOCATION = col_character(),
##   INDICATOR = col_character(),
##   SUBJECT = col_character(),
##   MEASURE = col_character(),
##   FREQUENCY = col_character(),
##   TIME = col_double(),
##   Value = col_double(),
##   `Flag Codes` = col_logical()
## )

df_debt_ratio <- df_debt_ratio %>% rename(debt_gdp = Value)

df_debt_wide <- df_debt_ratio %>% 
  select(-`Flag Codes`) %>% 
  pivot_wider(names_from = MEASURE, values_from = debt_gdp) %>% 
  select(-INDICATOR)
df_debt_wide

## # A tibble: 799 x 5
##    LOCATION SUBJECT FREQUENCY  TIME PC_GDP
##    <chr>    <chr>   <chr>     <dbl>  <dbl>
##  1 AUS      TOT     A          1995   57.6
##  2 AUS      TOT     A          1996   55.4
##  3 AUS      TOT     A          1997   54.6
##  4 AUS      TOT     A          1998   52.4
##  5 AUS      TOT     A          1999   44.8
##  6 AUS      TOT     A          2000   41.1
##  7 AUS      TOT     A          2001   40.4
##  8 AUS      TOT     A          2002   38.7
##  9 AUS      TOT     A          2003   35.7
## 10 AUS      TOT     A          2004   32.3
## # … with 789 more rows

Merging dataframes (long term interest rates, debt-to-gdp ratio and gdp)

df_merged <- df_gdp_wide %>% 
  left_join(df_ltint_wide %>% select(LOCATION, TIME, LTINT), by = c("LOCATION","TIME")) %>% 
  left_join(df_debt_wide %>% select(LOCATION, TIME, PC_GDP), by = c("LOCATION","TIME")) %>% 
  rename(debt_gdp = PC_GDP)
df_merged

## # A tibble: 2,485 x 8
##    LOCATION SUBJECT FREQUENCY  TIME GDP_MLN_USD GDP_USD_CAP LTINT debt_gdp
##    <chr>    <chr>   <chr>     <dbl>       <dbl>       <dbl> <dbl>    <dbl>
##  1 AUS      TOT     A          1960      25035.       2409.    NA       NA
##  2 AUS      TOT     A          1961      25326.       2380.    NA       NA
##  3 AUS      TOT     A          1962      27913.       2574.    NA       NA
##  4 AUS      TOT     A          1963      30386.       2748.    NA       NA
##  5 AUS      TOT     A          1964      32694.       2898.    NA       NA
##  6 AUS      TOT     A          1965      34490.       2998.    NA       NA
##  7 AUS      TOT     A          1966      38285.       3271.    NA       NA
##  8 AUS      TOT     A          1967      41004.       3442.    NA       NA
##  9 AUS      TOT     A          1968      46488.       3828.    NA       NA
## 10 AUS      TOT     A          1969      52132.       4202.    NA       NA
## # … with 2,475 more rows

Creating a natural log of GDP variable

df_merged <- df_merged %>%
  mutate(ln_gdp = log(GDP_MLN_USD)) 

Creating dataset from 2006 (date where long term interest rates data starts)

df_merged_2006_2020 <- df_merged %>% filter(TIME>=2006)
df_merged_2006_2020 %>% write_csv("gdp_ltint_debt2006_2020.csv")

Comparing what countries are in each dataframe

countries_gdp <- df_gdp %>% select(LOCATION) %>% unique()
countries_debt <- df_debt_ratio %>% select(LOCATION) %>% unique()
countries_ltint <- df_ltint %>% select(LOCATION) %>% unique()

setdiff(countries_gdp, countries_debt)

## # A tibble: 29 x 1
##    LOCATION
##    <chr>   
##  1 KOR     
##  2 NZL     
##  3 CHN     
##  4 IND     
##  5 IDN     
##  6 RUS     
##  7 ZAF     
##  8 DEW     
##  9 EU28    
## 10 OECD    
## # … with 19 more rows

setdiff(countries_debt, countries_ltint)

## # A tibble: 2 x 1
##   LOCATION
##   <chr>   
## 1 TUR     
## 2 EST

setdiff(countries_gdp, countries_ltint)

## # A tibble: 24 x 1
##    LOCATION
##    <chr>   
##  1 TUR     
##  2 CHN     
##  3 EST     
##  4 IDN     
##  5 DEW     
##  6 EU28    
##  7 OECD    
##  8 OECDE   
##  9 BRA     
## 10 SAU     
## # … with 14 more rows

df_merged_2006_2020 %>% summary(debt_gdp)

##    LOCATION           SUBJECT           FREQUENCY              TIME     
##  Length:900         Length:900         Length:900         Min.   :2006  
##  Class :character   Class :character   Class :character   1st Qu.:2009  
##  Mode  :character   Mode  :character   Mode  :character   Median :2013  
##                                                           Mean   :2013  
##                                                           3rd Qu.:2016  
##                                                           Max.   :2020  
##                                                                         
##   GDP_MLN_USD        GDP_USD_CAP         LTINT            debt_gdp      
##  Min.   :    9439   Min.   :  1551   Min.   :-0.5238   Min.   :  7.196  
##  1st Qu.:  180282   1st Qu.: 20406   1st Qu.: 1.4919   1st Qu.: 45.547  
##  Median :  443502   Median : 33335   Median : 3.3596   Median : 67.829  
##  Mean   : 3254583   Mean   : 33862   Mean   : 3.7163   Mean   : 76.844  
##  3rd Qu.: 2174240   3rd Qu.: 43884   3rd Qu.: 5.2444   3rd Qu.:103.304  
##  Max.   :63079189   Max.   :120670   Max.   :22.4975   Max.   :238.726  
##                     NA's   :14       NA's   :332       NA's   :427      
##      ln_gdp      
##  Min.   : 9.153  
##  1st Qu.:12.102  
##  Median :13.002  
##  Mean   :13.251  
##  3rd Qu.:14.592  
##  Max.   :17.960  
## 

median_debt <- median(df_merged_2006_2020$debt_gdp, na.rm=T)

Creating a eurozone countries variable

eurozone <- c("AUT","BEL","CYP","EST","FIN","FRA","DEU","GRC","IRL","ITA","LVA","LTU","LUX","MLT","NLD","PRT","SVK","SVN","ESP")

Creating and above-below variable for median of debt-to-gdp in 2013

median_debt_euro_2019 <- df_merged_2006_2020 %>% 
  filter(TIME==2013, LOCATION %in% eurozone) %>% 
  pull(debt_gdp) %>% median(na.rm=T)

Creating a comparators variable for those with similar debt-to-gdp ratios

comp_debt_gdp <- c("BEL","FRA","DEU","IRL","LUX","MLT","NLD","SVK","ESP")

Data exploring

Debt-to-gdp ratio data for 2020 is very patchy so 2019 will be used as the most recent data point

Plot 1

Visualising the relationship between GDP and long term interest rates in 2019 Colour of the dot corresponds to whether the country is above or below the median debt-to-gdp ratio in 2019

median_debt_2019 <- df_merged_2006_2020 %>% 
  filter(TIME==2019, LOCATION!="EU27_2020", LOCATION!="OECDE", LOCATION!="EA19") %>% 
  pull(debt_gdp) %>% median(na.rm=T)

p1 <- df_merged_2006_2020 %>% 
    filter(TIME==2019, LOCATION!="EU27_2020", LOCATION!="OECDE", LOCATION!="EA19") %>% 
    mutate(above_debt_median=if_else(debt_gdp>=median_debt_2019,TRUE,FALSE)) %>% 
    ggplot(aes(LTINT, GDP_MLN_USD, colour=above_debt_median, group=LOCATION)) + 
    geom_point()
ggplotly(p1) 

Visualising the relationship between GDP and long term interest rates in 2013

median_debt_2013 <- df_merged_2006_2020 %>% 
  filter(TIME==2013, LOCATION!="EU27_2020", LOCATION!="OECDE", LOCATION!="EA19") %>% 
  pull(debt_gdp) %>% median(na.rm=T)

Plot 2

Scatterplot - Colour of the dot corresponds to whether the country is above or below the median debt-to-gdp ratio in 2013

p2 <- df_merged_2006_2020 %>% 
    filter(TIME==2013, LOCATION!="EU27_2020", LOCATION!="OECDE", LOCATION!="EA19") %>% 
    mutate(above_debt_median=if_else(debt_gdp>=median_debt_2013,TRUE,FALSE)) %>% 
    ggplot(aes(LTINT, GDP_MLN_USD, colour=above_debt_median, group=LOCATION)) + 
    geom_point()
ggplotly(p2) 

Plot 3

Visualising long term interest rates and GDP relationship, colour-coded by above or below median of eurozone debt-to-gdp ratio

p3 <- df_merged_2006_2020 %>% 
    filter(TIME==2019, LOCATION %in% eurozone) %>% 
    mutate(above_debt_median=if_else(debt_gdp>=median_debt_euro_2019,TRUE,FALSE)) %>% 
    ggplot(aes(LTINT, GDP_MLN_USD, colour=above_debt_median, group=LOCATION)) + 
    geom_point()
ggplotly(p3) 

Plot 4

Visualising long term interest rates and debt-to-gdp ratio relationship in eurozone

p4 <- df_merged_2006_2020 %>% 
    filter(TIME==2019, LOCATION %in% eurozone) %>% 
    ggplot(aes(LTINT, debt_gdp, colour=LOCATION)) + 
    geom_point()
ggplotly(p4) 

Plot 5

Visualising GDP and debt-to-gdp ratio relationship in eurozone to choose a good comparator (similar debt-to-gdp ratio but different sizes)

p5 <- df_merged_2006_2020 %>% 
    filter(TIME==2019, LOCATION %in% eurozone) %>% 
    ggplot(aes(GDP_MLN_USD, debt_gdp, colour=LOCATION)) + 
    geom_point()
ggplotly(p5) 

Thoughts

• We will probably have to use 2019 as the most recent data point throughout because debt-to-gdp data for 2020 is patchy here.
• We can also do analysis for 2020 with the other variables.
• We can do everything just for the eurozone to eliminate noise and the effect of the US as an outlier but we might also choose to do EU (including non-eurozone) and UK to get a bit more info but also eliminate less noise
• Would be good to decide whether the UK always being there is necessary, I think it would probably be good.
• I used the median as the comparison point for debt-to-gdp ratio but we might want to choose something else.
• 2013 does not look that different from 2019 from what I can tell.
• To visualise I can’t think of much else beyond scatterplots, need to pick whether independent variable we care most about is debt-to-gdp ratio or GDP (or both). Could do two different plots, could do debt-to-gdpXlong term interest rates with GDP as size of the dots.

Comparators

• Option 1: Theory-driven comparators. SGC used New Zealand,
• Option 2: Countries that have similar debt-to-gdp ratios but very different GDPs, to see how they compare in terms of long term interest rates. Countries with similar debt-to-gdp ratios to Germany might make for good comparators with very different GDPs. Maybe the Netherlands, Finland, Ireland, Slovakia. See plot 5.

Plot 6

Same as plot 3 but with natural log of GDP Visualising long term interest rates and natural log of GDP relationship, colour-coded by above or below median of eurozone debt-to-gdp ratio

p6 <- df_merged_2006_2020 %>% 
    filter(TIME==2019, LOCATION %in% eurozone) %>% 
    mutate(above_debt_median=if_else(debt_gdp>=median_debt_euro_2019,TRUE,FALSE)) %>% 
    ggplot(aes(LTINT, ln_gdp, colour=above_debt_median, group=LOCATION)) + 
    geom_point()
ggplotly(p6) 

Plot 7

Visualising relationship between debt-to-GDP ratio and long term interest rates with size of bubbles representing the natural log of GDP

p7 <- df_merged_2006_2020 %>% 
    filter(TIME==2019, LOCATION %in% eurozone) %>% 
    ggplot(aes(x = LTINT, y = debt_gdp, size = ln_gdp,    colour=LOCATION)) + 
    geom_point(alpha=0.7)
ggplotly(p7) 

Plot 8

Visualising relationship between natural log of GDP and long term interest rates only using countries with similar debt-to-GDP ratios

p8 <- df_merged_2006_2020 %>% 
    filter(TIME==2019, LOCATION %in% comp_debt_gdp) %>% 
    ggplot(aes(x = LTINT, y = ln_gdp, colour=LOCATION)) + 
    geom_point(alpha=0.7)
ggplotly(p8)